Individual skill

implementing-network-traffic-baselining

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling

internal/tools/skills/data/network-security/implementing-network-traffic-baselining/SKILL.md

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Last checked: Nov 7, 2026

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Repository stars (not skill ratings): 1,313
Repository last updated: Nov 7, 2026
License: Apache-2.0

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cybersecurity Repository topic
security Repository topic
bug-bounty Repository topic
automation Repository topic
autonomous-pentesting Repository topic
ai-agent Repository topic
ai-security Repository topic
ethical-hacking Repository topic
golang Repository topic
penetration-testing Repository topic
netflow Declared skill tag
ipfix Declared skill tag
traffic-analysis Declared skill tag
baselining Declared skill tag
anomaly-detection Declared skill tag
pandas Declared skill tag
network-monitoring Declared skill tag
data Derived category

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# Implementing Network Traffic Baselining

## Overview

Network traffic baselining establishes normal communication patterns by analyzing historical NetFlow/IPFIX data to create statistical profiles of expected behavior. This skill uses Python pandas to compute hourly and daily traffic distributions, per-host byte/packet counts, protocol ratios, and top-N talker profiles. Anomalies are detected using z-score thresholds and IQR (interquartile range) outlier methods, enabling SOC analysts to identify deviations such as data exfiltration spikes, beaconing patterns, and unusual port usage.


## When to Use

- When deploying or configuring implementing network traffic baselining capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation